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NiuTrans.Tensor
Commit bdf5c952 by xuchen
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update setdata by template and optimize some interface

parent bd05b21b
正在显示 包含 77 行增加 和 505 行删除
source/Main.cpp
...@@ -63,155 +63,3 @@ int main( int argc, const char ** argv ) ...@@ -63,155 +63,3 @@ int main( int argc, const char ** argv )
return 0; return 0;
} }
void BackwardTest()
{
XNet net;
XTensor a;
XTensor b;
XTensor c;
a.enableGrad = true;
b.enableGrad = false;
c.enableGrad = false;
XTensor mean;
XTensor origin;
InitTensor2DV2(&a, 2, 3);
InitTensor1DV2(&b, 2);
a.SetZeroAll();
b.SetZeroAll();
a.Set2D(1.0F, 0, 0);
a.Set2D(2.0F, 0, 1);
a.Set2D(3.0F, 0, 2);
a.Set2D(4.0F, 1, 0);
a.Set2D(5.0F, 1, 1);
a.Set2D(6.0F, 1, 2);
b.Set1D(2.0F, 0);
b.Set1D(1.0F, 1);
DivDim(a, b, c, 0);
c.Dump(stderr, "c:");
auto loss = CrossEntropy(c, a);
//XLink::ShowNetwork(stderr, &c);
net.Backward(loss);
a.grad->Dump(stderr);
}
void TransposeTest()
{
#ifdef USE_CUDA
XMem mem0(0, UNI_FREE, MILLION * 64, 1024, MILLION * 64);
//XMem mem1(1, UNI_FREE, MILLION * 64, 1024, MILLION * 64);
XTensor x;
XTensor y;
XTensor z;
int loops = 2000;
int B = 3 * 2 * 4;
int K = 8 * 1;
int N = 50;
int H = 512 * 4;
int nnn = GDevs.nGPU;
InitTensor3DV2(&x, B, N, H, X_FLOAT, 0);
InitTensor4DV2(&y, K, B, N, H/K, X_FLOAT, 0);
InitTensor3DV2(&z, B, N, H, X_FLOAT, 0);
cudaEvent_t ctime0;
cudaEvent_t ctime1;
cudaEvent_t ctime2;
cudaEvent_t ctime3;
cudaEvent_t ctime4;
cudaEvent_t ctime5;
float elapsedSplit = 0.0;
float elapsedMerge = 0.0;
float elapsedSum = 0.0;
cudaEventCreate(&ctime0);
cudaEventCreate(&ctime1);
cudaEventCreate(&ctime2);
cudaEventCreate(&ctime3);
cudaEventCreate(&ctime4);
cudaEventCreate(&ctime5);
cudaEventRecord(ctime0, 0);
double time0 = GetClock();
for(int i = 0; i < loops; i++)
_Split(&x, &y, 2, K);
double time1 = GetClock();
cudaEventRecord(ctime1, 0);
cudaEventSynchronize(ctime1);
cudaEventElapsedTime(&elapsedSplit, ctime0, ctime1);
cudaEventRecord(ctime2, 0);
double time2 = GetClock();
for(int i = 0; i < loops; i++)
_Merge(&y, &x, 3);
double time3 = GetClock();
cudaEventRecord(ctime3, 0);
cudaEventSynchronize(ctime3);
cudaEventElapsedTime(&elapsedMerge, ctime2, ctime3);
cudaEventRecord(ctime4, 0);
double time4 = GetClock();
for(int i = 0; i < loops; i++)
_Sum(&x, &z, &x);
double time5 = GetClock();
cudaEventRecord(ctime5, 0);
cudaEventSynchronize(ctime5);
cudaEventElapsedTime(&elapsedSum, ctime4, ctime5);
fprintf(stderr, "split:%f merge:%f sum:%f\n", time1 - time0, time3 - time2, time5 - time4);
fprintf(stderr, "split:%f merge:%f sum:%f\n", elapsedSplit, elapsedMerge, elapsedSum);
#endif
}
void SumDimTest()
{
XTensor x;
XTensor y;
XTensor z;
int a = 5;
int b = 7;
int c = 3;
InitTensor3DV2(&x, a, b, c, X_FLOAT, -1);
InitTensor1DV2(&y, c, X_FLOAT, -1);
InitTensor3DV2(&z, a, b, c, X_FLOAT, -1);
x.SetZeroAll();
y.SetZeroAll();
z.SetZeroAll();
DTYPE * data = new DTYPE[x.unitNum];
for(int i = 0; i < x.unitNum; i++)
data[i] = (DTYPE)i;
x.SetData(data, x.unitNum);
for(int i = 0; i < y.unitNum; i++)
data[i] = -(DTYPE)i;
y.SetData(data, y.unitNum);
_SumDim(&x, &y, &z, 2);
z.Dump(stderr, "z:");
delete[] data;
}
source/network/XBackwardLoss.cpp
...@@ -33,7 +33,6 @@ ...@@ -33,7 +33,6 @@
namespace nts{ namespace nts{
/* compute dE/dx of a node */ /* compute dE/dx of a node */
void XLossGrad::MakeGrad(XTensor * node, bool isEfficient) void XLossGrad::MakeGrad(XTensor * node, bool isEfficient)
{ {
...@@ -53,15 +52,7 @@ void XLossGrad::MakeGrad(XTensor * node, bool isEfficient) ...@@ -53,15 +52,7 @@ void XLossGrad::MakeGrad(XTensor * node, bool isEfficient)
XTensor * dedy = output->grad; XTensor * dedy = output->grad;
if (income.tailNum == 1) { if (income.tailNum == 1) {
if (dedy->dataType == X_FLOAT) dedy->SetDataFixed(1);
_SetDataFixedFloat(dedy, 1.0F);
else if (dedy->dataType == X_DOUBLE)
_SetDataFixedDouble(dedy, 1.0);
else if (dedy->dataType == X_INT)
_SetDataFixedInt(dedy, 1);
else
ShowNTErrors("TODO");
return; return;
} }
......
source/tensor/Main.cpp deleted 100644 → 0
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northestern University.
* All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
*
* This is the entrance of the low-level tensor library : NiuTrans.Tensor
*
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2015-12-14
*
*/
#include <stdio.h>
#include <math.h>
#include <time.h>
#include "XTensor.h"
#include "XDevice.h"
#include "./test/Test.h"
#include "./core/CHeader.h"
#include "./XBLAS.h"
#include "./core/sort/TopK.h"
#include "./core/movement/Gather.h"
//#define CRTDBG_MAP_ALLOC
//#include <stdlib.h>
//#include <crtdbg.h>
using namespace nts;
void SmallTest();
void TransposeTest();
void PowerTest();
int main( int argc, const char ** argv )
{
//PowerTest();
//LittleTest();
//T2TTest();
//T2TTest2();
//return 0;
//_CrtSetBreakAlloc(123);
/* a tiny test */
//SmallTest();
//_CrtDumpMemoryLeaks();
//return 0;
if(argc > 1 && !strcmp(argv[1], "-test"))
Test();
else{
fprintf(stderr, "Thanks for using NiuTrans.Tensor! This is a library that eases the\n");
fprintf(stderr, "use of tensors. All you need is to ... \n\n");
fprintf(stderr, "Run this program with \"-test\" for unit test!\n");
}
//_CrtDumpMemoryLeaks();
return 0;
}
void myRead(XTensor * tensor, const char * filename, const char * label)
{
FILE * file = fopen(filename, "rb");
if(file == NULL)
printf("%s\n", filename);
tensor->Read(file, label);
}
void myDump(XTensor * tensor, const char * filename, const char * label)
{
FILE * file = fopen(filename, "wb");
if(file == NULL)
printf("%s\n", filename);
tensor->Dump(file, label);
}
void PowerTest()
{
XTensor input;
XTensor output;
InitTensor2D(&input, 256, 10000, X_FLOAT, 0);
InitTensor2D(&output, 256, 10000, X_FLOAT, 0);
myRead(&input, "1.txt", "");
_Power(&input, &output, 2);
output.Dump(stderr, "", 200);
}
void SmallTest()
{
XTensor a;
XTensor b;
XTensor c;
XTensor d;
InitTensor2D(&a, 2, 2);
InitTensor2D(&b, 2, 2);
a.SetZeroAll();
b.SetZeroAll();
a.Set2D(1.0F, 0, 0);
a.Set2D(2.0F, 1, 1);
b = Sum(a, Multiply(a, a));
/* this is prohibited !!!!!!!!!!!!! */
//XTensor c = a * b + a;
//XTensor d = a + b + c.Lin(0.5F);
c = a * b + a;
d = a + b + c.Lin(0.5F);
XLink::CheckNetwork(&d);
//XLink::ShowNetwork(stderr, &d);
a.Dump(stderr, "a:");
b.Dump(stderr, "b:");
c.Dump(stderr, "c:");
d.Dump(stderr, "d:");
}
void TransposeTest()
{
XTensor a;
XTensor b;
int I = 2;
int J = 3;
InitTensor4D(&a, 2, 3, 4, 5);
int * dims = new int[a.order];
memcpy(dims, a.dimSize, sizeof(int) * a.order);
dims[I] = a.dimSize[J];
dims[J] = a.dimSize[I];
InitTensor(&b, 4, dims);
a.SetZeroAll();
b.SetZeroAll();
float * data = new float[a.unitNum];
for(int i = 0; i < a.unitNum; i++)
data[i] = (float)i;
a.SetData(data, a.unitNum, 0);
_Transpose(&a, &b, I, J);
b.Dump(stderr, "b:");
delete[] data;
}
source/tensor/XTensor.cpp
...@@ -526,6 +526,8 @@ void XTensor::SetDevice(int myDevId, XMem * myMem) ...@@ -526,6 +526,8 @@ void XTensor::SetDevice(int myDevId, XMem * myMem)
} }
else { else {
myMem = GMems.GetMem(myDevId); myMem = GMems.GetMem(myDevId);
FlushToMem(myMem);
isInGlobalMem = false;
} }
} }
...@@ -818,6 +820,16 @@ void XTensor::Range(DTYPE lower, DTYPE upper, DTYPE step) ...@@ -818,6 +820,16 @@ void XTensor::Range(DTYPE lower, DTYPE upper, DTYPE step)
_SetDataRange(this, lower, upper, step); _SetDataRange(this, lower, upper, step);
} }
/* generate data items with a fixed value */
template<class T>
void XTensor::SetDataFixed(T num)
{
_SetDataFixed(this, num);
}
template void XTensor::SetDataFixed<int>(int);
template void XTensor::SetDataFixed<float>(float);
template void XTensor::SetDataFixed<double>(double);
/* /*
set the tensor items by a uniform distribution in range [lower, upper] set the tensor items by a uniform distribution in range [lower, upper]
>> lower - lower value of the range >> lower - lower value of the range
...@@ -825,62 +837,7 @@ set the tensor items by a uniform distribution in range [lower, upper] ...@@ -825,62 +837,7 @@ set the tensor items by a uniform distribution in range [lower, upper]
*/ */
void XTensor::SetDataRand(DTYPE lower, DTYPE upper) void XTensor::SetDataRand(DTYPE lower, DTYPE upper)
{ {
// TODO: GPU code!!!!!!! _SetDataRand(this, lower, upper);
if (data == NULL)
return;
// srand((unsigned)time(0));
DTYPE variance = upper - lower;
void * d = NULL;
if (dataType == X_FLOAT) {
d = new float[unitNum];
for (int i = 0; i < unitNum; i++) {
DTYPE value = lower + variance * (float)rand() / RAND_MAX;
*((float*)d + i) = value;
}
}
else if (dataType == X_DOUBLE) {
d = new double[unitNum];
for (int i = 0; i < unitNum; i++) {
*((double*)d + i) = lower + variance * rand() / RAND_MAX;
}
}
else {
ShowNTErrors("Data type must be X_FLOAT or X_Double!");
}
SetData(d, unitNum);
if (dataType == X_FLOAT) {
delete[] (float*)d;
}
else {
delete[] (double*)d;
}
}
/* a gauss distribution (Box-Muller method) */
double GaussRand(DTYPE mean, DTYPE standardDeviation)
{
// TODO: GPU code!!!!!!!
static double u, v;
static int phase = 0;
double z;
double pi = 3.141592654;
if (phase == 0){
u = (rand() + 1.0) / (RAND_MAX + 1.0);
v = (rand() + 1.0) / (RAND_MAX + 1.0);
z = sqrt(-2.0 * log(u))* sin(2.0 * pi * v);
}
else{
z = sqrt(-2.0 * log(u)) * cos(2.0 * pi * v);
}
phase = 1 - phase;
return mean + (z * standardDeviation);
} }
/* /*
...@@ -890,37 +847,7 @@ set the tensor items by a normal distribution ...@@ -890,37 +847,7 @@ set the tensor items by a normal distribution
*/ */
void XTensor::SetDataRandn(DTYPE mean, DTYPE standardDeviation) void XTensor::SetDataRandn(DTYPE mean, DTYPE standardDeviation)
{ {
// TODO: cuda code!!!!!!! _SetDataRandN(this, mean, standardDeviation);
if (data == NULL)
return;
// srand((unsigned)time(0));
void * d = NULL;
if (dataType == X_FLOAT) {
d = new float[unitNum];
for (int i = 0; i < unitNum; i++) {
*((float*)d + i) = (float)GaussRand(mean, standardDeviation);
}
}
else if (dataType == X_DOUBLE) {
d = new double[unitNum];
for (int i = 0; i < unitNum; i++) {
*((double*)d + i) = GaussRand(mean, standardDeviation);
}
}
else {
ShowNTErrors("Data type must be X_FLOAT or X_Double!");
}
SetData(d, unitNum);
if (dataType == X_FLOAT) {
delete[] (float*)d;
}
else {
delete[] (double*)d;
}
} }
/* /*
......
source/tensor/XTensor.h
...@@ -28,6 +28,7 @@ ...@@ -28,6 +28,7 @@
#ifndef __XTENSOR_H__ #ifndef __XTENSOR_H__
#define __XTENSOR_H__ #define __XTENSOR_H__
#include <math.h>
#include "XGlobal.h" #include "XGlobal.h"
#include "XMem.h" #include "XMem.h"
#include "XPRunner.h" #include "XPRunner.h"
...@@ -303,6 +304,10 @@ public: ...@@ -303,6 +304,10 @@ public:
/* generate data items with a range by start, end and the step */ /* generate data items with a range by start, end and the step */
void Range(DTYPE lower, DTYPE upper, DTYPE step); void Range(DTYPE lower, DTYPE upper, DTYPE step);
/* generate data items with a fixed value */
template<class T>
void SetDataFixed(T num);
/* set tensor items by a uniform distribution */ /* set tensor items by a uniform distribution */
void SetDataRand(DTYPE lower = 0.0F, DTYPE upper = 1.0F); void SetDataRand(DTYPE lower = 0.0F, DTYPE upper = 1.0F);
......
source/tensor/core/CHeader.h
...@@ -91,9 +91,9 @@ ...@@ -91,9 +91,9 @@
#include "sort/Sort.h" #include "sort/Sort.h"
#include "sort/TopK.h" #include "sort/TopK.h"
#include "utilities/XMatrixSegment.h"
#include "utilities/FlushToMem.h"
#include "utilities/CheckData.h" #include "utilities/CheckData.h"
#include "utilities/FlushToMem.h"
#include "utilities/SetAscendingOrder.h" #include "utilities/SetAscendingOrder.h"
#include "utilities/XMatrixSegment.h"
#endif // __CHEADER_H__ #endif // __CHEADER_H__
source/tensor/core/getandset/OnehotAndIndex.cpp
...@@ -116,7 +116,7 @@ void _IndexToOnehot(const XTensor * index, XTensor * onehot, ...@@ -116,7 +116,7 @@ void _IndexToOnehot(const XTensor * index, XTensor * onehot,
float confidence = 1 - labelSmoothingP; float confidence = 1 - labelSmoothingP;
float lowconfidence = labelSmoothingP / size; float lowconfidence = labelSmoothingP / size;
_SetDataFixedFloat(onehot, lowconfidence); onehot->SetDataFixed(lowconfidence);
#ifdef USE_CUDA #ifdef USE_CUDA
if(onehot->devID >= 0 && index->devID >= 0) { if(onehot->devID >= 0 && index->devID >= 0) {
......
source/tensor/core/getandset/SetData.cuh
...@@ -28,31 +28,24 @@ ...@@ -28,31 +28,24 @@
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
/* generate data items with a fixed value p (in int) */ /* generate data items with a fixed value */
void _CudaSetDataFixedInt(XTensor * tensor, int p); template<class T>
void _CudaSetDataFixed(XTensor * tensor, T value);
/* generate data items with a fixed value p (in float) */ /* generate data items with a fixed value p
void _CudaSetDataFixedFloat(XTensor * tensor, float p); only if the condition entry is non-zero */
template<class T>
/* generate data items with a fixed value p (in double) */ void _CudaSetDataFixedCond(XTensor * tensor, XTensor * condition, T p);
void _CudaSetDataFixedDouble(XTensor * tensor, double p);
/* generate data items with a fixed value p (in float) only
if the condition entry is non-zero */
void _CudaSetDataFixedCondFloat(XTensor * tensor, XTensor * condition, float p);
/* generate data items with a fixed value p (in int) only
if the condition entry is non-zero */
void _CudaSetDataFixedCondInt(XTensor * tensor, XTensor * condition, int p);
/* set data items along with a given dimension (and keep the remaining items unchanged) */ /* set data items along with a given dimension (and keep the remaining items unchanged) */
void _CudaSetDataDim(XTensor * tensor, int beg, int len, int dim, DTYPE p); template<class T>
void _CudaSetDataDim(XTensor * tensor, int beg, int len, int dim, T p);
/* modify data items along with a given index and dimension (and keep the remaining items unchanged) */ /* modify data items along with a given index and dimension (and keep the remaining items unchanged) */
void _CudaSetDataIndexed(XTensor * source, XTensor * modify, int dim, int index); void _CudaSetDataIndexed(XTensor * source, XTensor * modify, int dim, int index);
/* generate data as lower triangular matrics for last two dimensions (cuda version) */ /* generate data as lower triangular matrics for last two dimensions (cuda version) */
void _CudaSetDataLowTri(XTensor * tensor, DTYPE p, int shift); void _CudaSetDataLowTri(XTensor * tensor, DTYPE value, int shift);
/* generate data items with a uniform distribution in [lower, upper] */ /* generate data items with a uniform distribution in [lower, upper] */
void _CudaSetDataRand(const XTensor * tensor, DTYPE lower, DTYPE upper); void _CudaSetDataRand(const XTensor * tensor, DTYPE lower, DTYPE upper);
......
source/tensor/core/getandset/SetData.h
...@@ -30,32 +30,17 @@ namespace nts { // namespace nts(NiuTrans.Tensor) ...@@ -30,32 +30,17 @@ namespace nts { // namespace nts(NiuTrans.Tensor)
/* generate data items with a xavier initialization */ /* generate data items with a xavier initialization */
void _SetDataFanInOut(XTensor * tensor, DTYPE gain = 1.0F); void _SetDataFanInOut(XTensor * tensor, DTYPE gain = 1.0F);
/* generate data items with a fixed value p */ /* generate data items with a fixed value */
void _SetDataFixed(XTensor * tensor, void * valuePointer); template<class T>
void _SetDataFixed(XTensor * tensor, T value);
/* generate data items with a fixed value p (in default type) */ /* generate data items with a fixed value only if the condition entry is non-zero */
void SetDataFixed(XTensor &tensor, DTYPE p); template<class T>
void _SetDataFixedCond(XTensor* tensor, XTensor* condition, T value);
/* generate data items with a fixed value p (in integer) */
void SetDataFixedInt(XTensor &tensor, int p);
/* generate data items with a fixed value p (in int) */
void _SetDataFixedInt(XTensor * tensor, int p);
/* generate data items with a fixed value p (in float) */
void _SetDataFixedFloat(XTensor * tensor, float p);
/* generate data items with a fixed value p (in double) */
void _SetDataFixedDouble(XTensor * tensor, double p);
/* generate data items with a fixed value p only if the condition entry is non-zero */
void _SetDataFixedCond(XTensor * tensor, XTensor * condition, DTYPE p);
/* generate data items with a fixed value p only if the condition entry is non-zero */
void _SetDataFixedCondInt(XTensor * tensor, XTensor * condition, int p);
/* set data items along with a given dimension (and keep the remaining items unchanged) */ /* set data items along with a given dimension (and keep the remaining items unchanged) */
void _SetDataDim(XTensor * tensor, int beg, int len, int dim, DTYPE p); template<class T>
void _SetDataDim(XTensor * tensor, int beg, int len, int dim, T p);
/* modify data items along with a given index and dimension (and keep the remaining items unchanged) */ /* modify data items along with a given index and dimension (and keep the remaining items unchanged) */
void _SetDataIndexed(XTensor * source, XTensor * modify, int dim, int index); void _SetDataIndexed(XTensor * source, XTensor * modify, int dim, int index);
......
source/tensor/function/DropoutWithIndex.cpp
...@@ -70,7 +70,7 @@ XTensor DropoutWithIndex(const XTensor &x, XTensor &maskIndex, DTYPE scale) ...@@ -70,7 +70,7 @@ XTensor DropoutWithIndex(const XTensor &x, XTensor &maskIndex, DTYPE scale)
InitTensor1DV2(&c, x.unitNum, x.dataType, x.devID, x.mem); InitTensor1DV2(&c, x.unitNum, x.dataType, x.devID, x.mem);
_SetDataFixedFloat(&c, 1.0F); c.SetDataFixed(1.0);
_DropoutWithIndex(&x, &maskIndex, &c); _DropoutWithIndex(&x, &maskIndex, &c);
......
source/tensor/function/Loss.cpp
...@@ -383,15 +383,7 @@ void _LossBackward(XTensor * dedy, XTensor * t, XTensor * y, ...@@ -383,15 +383,7 @@ void _LossBackward(XTensor * dedy, XTensor * t, XTensor * y,
int leadDim, int tBeg, int tLen, int yBeg) int leadDim, int tBeg, int tLen, int yBeg)
{ {
if(t == NULL){ if(t == NULL){
if(dedy->dataType == X_FLOAT) dedy->SetDataFixed(1);
_SetDataFixedFloat(dedy, 1.0F);
else if(dedy->dataType == X_DOUBLE)
_SetDataFixedDouble(dedy, 1.0);
else if(dedy->dataType == X_INT)
_SetDataFixedInt(dedy, 1);
else{
ShowNTErrors("TODO");
}
return; return;
} }
......
source/tensor/test/TDropout.cpp
...@@ -50,7 +50,7 @@ bool TestDropout1() ...@@ -50,7 +50,7 @@ bool TestDropout1()
XTensor yUser; XTensor yUser;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(x, 1.0F); x->SetDataFixed(1);
y->SetZeroAll(); y->SetZeroAll();
/* call Dropout function */ /* call Dropout function */
...@@ -88,7 +88,7 @@ bool TestDropout1() ...@@ -88,7 +88,7 @@ bool TestDropout1()
XTensor yUserGPU; XTensor yUserGPU;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(xGPU, 1.0F); xGPU->SetDataFixed(1);
yGPU->SetZeroAll(); yGPU->SetZeroAll();
/* call Dropout function */ /* call Dropout function */
...@@ -157,10 +157,10 @@ bool TestDropout2() ...@@ -157,10 +157,10 @@ bool TestDropout2()
XTensor * dedy = NewTensorV2(order, dimSize); XTensor * dedy = NewTensorV2(order, dimSize);
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(x, 1.0F); x->SetDataFixed(1.0);
y->SetZeroAll(); y->SetZeroAll();
dedx->SetZeroAll(); dedx->SetZeroAll();
_SetDataFixedFloat(dedy, 1.5F); dedy->SetDataFixed(1.5);
/* call Dropout function */ /* call Dropout function */
float dropProb = 0.5F; float dropProb = 0.5F;
...@@ -183,10 +183,10 @@ bool TestDropout2() ...@@ -183,10 +183,10 @@ bool TestDropout2()
XTensor * dedyGPU = NewTensorV2(order, dimSize, X_FLOAT, 1.0F, 0); XTensor * dedyGPU = NewTensorV2(order, dimSize, X_FLOAT, 1.0F, 0);
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(xGPU, 1.0F); xGPU->SetDataFixed(1.0);
yGPU->SetZeroAll(); yGPU->SetZeroAll();
dedxGPU->SetZeroAll(); dedxGPU->SetZeroAll();
_SetDataFixedFloat(dedyGPU, 1.5F); dedyGPU->SetDataFixed(1.5);
/* call Dropout function */ /* call Dropout function */
_Dropout(xGPU, yGPU, seed, dropProb); _Dropout(xGPU, yGPU, seed, dropProb);
......
source/tensor/test/TReduceSum.cpp
...@@ -195,8 +195,8 @@ bool TestReduceSum2() ...@@ -195,8 +195,8 @@ bool TestReduceSum2()
XTensor tUser; XTensor tUser;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
_SetDataFixedFloat(answer, (float)s->GetDim(1)); answer->SetDataFixed(s->GetDim(1));
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(s, t, 1); _ReduceSum(s, t, 1);
...@@ -215,7 +215,7 @@ bool TestReduceSum2() ...@@ -215,7 +215,7 @@ bool TestReduceSum2()
XTensor tUserGPU; XTensor tUserGPU;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(sGPU, 1.0F); sGPU->SetDataFixed(1);
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(sGPU, tGPU, 1); _ReduceSum(sGPU, tGPU, 1);
...@@ -284,8 +284,8 @@ bool TestReduceSum3() ...@@ -284,8 +284,8 @@ bool TestReduceSum3()
XTensor tUser; XTensor tUser;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
_SetDataFixedFloat(answer, (float)s->GetDim(1)); answer->SetDataFixed(s->GetDim(1));
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(s, t, 1); _ReduceSum(s, t, 1);
...@@ -304,7 +304,7 @@ bool TestReduceSum3() ...@@ -304,7 +304,7 @@ bool TestReduceSum3()
XTensor tUserGPU; XTensor tUserGPU;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(sGPU, 1.0F); sGPU->SetDataFixed(1);
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(sGPU, tGPU, 1); _ReduceSum(sGPU, tGPU, 1);
...@@ -373,8 +373,8 @@ bool TestReduceSum4() ...@@ -373,8 +373,8 @@ bool TestReduceSum4()
XTensor tUser; XTensor tUser;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
_SetDataFixedFloat(answer, (float)s->GetDim(1)); answer->SetDataFixed(s->GetDim(1));
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(s, t, 1); _ReduceSum(s, t, 1);
...@@ -393,7 +393,7 @@ bool TestReduceSum4() ...@@ -393,7 +393,7 @@ bool TestReduceSum4()
XTensor tUserGPU; XTensor tUserGPU;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(sGPU, 1.0F); sGPU->SetDataFixed(1);
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(sGPU, tGPU, 1); _ReduceSum(sGPU, tGPU, 1);
...@@ -464,8 +464,8 @@ bool TestReduceSum5() ...@@ -464,8 +464,8 @@ bool TestReduceSum5()
XTensor tUser; XTensor tUser;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
_SetDataFixedFloat(answer, (float)s->GetDim(1)); answer->SetDataFixed(s->GetDim(1));
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(s, t, 1); _ReduceSum(s, t, 1);
...@@ -484,7 +484,7 @@ bool TestReduceSum5() ...@@ -484,7 +484,7 @@ bool TestReduceSum5()
XTensor tUserGPU; XTensor tUserGPU;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(sGPU, 1.0F); sGPU->SetDataFixed(1);
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(sGPU, tGPU, 1); _ReduceSum(sGPU, tGPU, 1);
...@@ -556,8 +556,8 @@ bool TestReduceSum6() ...@@ -556,8 +556,8 @@ bool TestReduceSum6()
XTensor tUser; XTensor tUser;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
_SetDataFixedFloat(answer, (float)s->GetDim(1)); answer->SetDataFixed(s->GetDim(1));
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(s, t, 1); _ReduceSum(s, t, 1);
...@@ -576,7 +576,7 @@ bool TestReduceSum6() ...@@ -576,7 +576,7 @@ bool TestReduceSum6()
XTensor tUserGPU; XTensor tUserGPU;
/* initialize variables */ /* initialize variables */
_SetDataFixedFloat(sGPU, 1.0F); sGPU->SetDataFixed(1);
/* call ReduceSum function */ /* call ReduceSum function */
_ReduceSum(sGPU, tGPU, 1); _ReduceSum(sGPU, tGPU, 1);
......
source/tensor/test/TSetData.cpp
...@@ -119,7 +119,7 @@ bool TestSetData2() ...@@ -119,7 +119,7 @@ bool TestSetData2()
XTensor * modify = NewTensorV2(dataOrder, dataDimSize); XTensor * modify = NewTensorV2(dataOrder, dataDimSize);
/* Initialize variables */ /* Initialize variables */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
modify->SetData(data, dataUnitNum); modify->SetData(data, dataUnitNum);
/* call SetDataIndexed function */ /* call SetDataIndexed function */
...@@ -137,7 +137,7 @@ bool TestSetData2() ...@@ -137,7 +137,7 @@ bool TestSetData2()
XTensor * modifyGPU = NewTensorV2(dataOrder, dataDimSize, X_FLOAT, 1.0F, 0); XTensor * modifyGPU = NewTensorV2(dataOrder, dataDimSize, X_FLOAT, 1.0F, 0);
/* Initialize variables */ /* Initialize variables */
_SetDataFixedFloat(sGPU, 1.0F); sGPU->SetDataFixed(1);
modifyGPU->SetData(data, dataUnitNum); modifyGPU->SetData(data, dataUnitNum);
/* call SetDataIndexed function */ /* call SetDataIndexed function */
...@@ -212,11 +212,11 @@ bool TestSetData3() ...@@ -212,11 +212,11 @@ bool TestSetData3()
XTensor * modify = NewTensorV2(dataOrder, dataDimSize); XTensor * modify = NewTensorV2(dataOrder, dataDimSize);
/* Initialize variables */ /* Initialize variables */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
modify->SetData(data, dataUnitNum); modify->SetData(data, dataUnitNum);
/* call SetDataIndexed function */ /* call SetDataIndexed function */
_SetDataFixedFloat(s, 1.0F); s->SetDataFixed(1);
_SetDataIndexed(s, modify, 1, 1); _SetDataIndexed(s, modify, 1, 1);
/* check results */ /* check results */
...@@ -231,7 +231,7 @@ bool TestSetData3() ...@@ -231,7 +231,7 @@ bool TestSetData3()
XTensor * modifyGPU = NewTensorV2(dataOrder, dataDimSize, X_FLOAT, 1.0F, 0); XTensor * modifyGPU = NewTensorV2(dataOrder, dataDimSize, X_FLOAT, 1.0F, 0);
/* Initialize variables */ /* Initialize variables */
_SetDataFixedFloat(sGPU, 1.0F); sGPU->SetDataFixed(1);
modifyGPU->SetData(data, dataUnitNum); modifyGPU->SetData(data, dataUnitNum);
/* call SetDataIndexed function */ /* call SetDataIndexed function */
......
source/tensor/test/TSpread.cpp
...@@ -91,7 +91,7 @@ bool TestSpread1() ...@@ -91,7 +91,7 @@ bool TestSpread1()
XTensor * modify = NewTensorV2(dataOrder, dataDimSize); XTensor * modify = NewTensorV2(dataOrder, dataDimSize);
/* Initialize variables */ /* Initialize variables */
_SetDataFixedFloat(s, 0.0F); s->SetZeroAll();
modify->SetData(data, dataUnitNum); modify->SetData(data, dataUnitNum);
/* call _Spread function */ /* call _Spread function */
...@@ -109,7 +109,7 @@ bool TestSpread1() ...@@ -109,7 +109,7 @@ bool TestSpread1()
XTensor * modifyGPU = NewTensorV2(dataOrder, dataDimSize, X_FLOAT, 1.0F, 0); XTensor * modifyGPU = NewTensorV2(dataOrder, dataDimSize, X_FLOAT, 1.0F, 0);
/* Initialize variables */ /* Initialize variables */
_SetDataFixedFloat(sGPU, 0.0F); sGPU->SetZeroAll();
modifyGPU->SetData(data, dataUnitNum); modifyGPU->SetData(data, dataUnitNum);
/* call _Spread function */ /* call _Spread function */
......
source/tensor/test/TSumDim.cpp
...@@ -296,8 +296,8 @@ bool TestSumDim3() ...@@ -296,8 +296,8 @@ bool TestSumDim3()
/* initialize variables */ /* initialize variables */
a->SetZeroAll(); a->SetZeroAll();
cMe->SetZeroAll(); cMe->SetZeroAll();
_SetDataFixedFloat(b, 1.0F); b->SetDataFixed(1);
_SetDataFixedFloat(answer, 1.0F); answer->SetDataFixed(1);
/* call SumDim function */ /* call SumDim function */
_SumDim(a, b, c, 1); _SumDim(a, b, c, 1);
...@@ -323,7 +323,7 @@ bool TestSumDim3() ...@@ -323,7 +323,7 @@ bool TestSumDim3()
/* Initialize variables */ /* Initialize variables */
aGPU->SetZeroAll(); aGPU->SetZeroAll();
cMe->SetZeroAll(); cMe->SetZeroAll();
_SetDataFixedFloat(bGPU, 1.0F); bGPU->SetDataFixed(1);
/* call sum function */ /* call sum function */
_SumDim(aGPU, bGPU, cGPU, 1); _SumDim(aGPU, bGPU, cGPU, 1);
...@@ -405,8 +405,8 @@ bool TestSumDim4() ...@@ -405,8 +405,8 @@ bool TestSumDim4()
/* initialize variables */ /* initialize variables */
a->SetZeroAll(); a->SetZeroAll();
cMe->SetZeroAll(); cMe->SetZeroAll();
_SetDataFixedFloat(b, 1.0F); b->SetDataFixed(1);
_SetDataFixedFloat(answer, 1.0F); answer->SetDataFixed(1);
/* call SumDim function */ /* call SumDim function */
_SumDim(a, b, c, 1); _SumDim(a, b, c, 1);
...@@ -432,7 +432,7 @@ bool TestSumDim4() ...@@ -432,7 +432,7 @@ bool TestSumDim4()
/* Initialize variables */ /* Initialize variables */
aGPU->SetZeroAll(); aGPU->SetZeroAll();
cMe->SetZeroAll(); cMe->SetZeroAll();
_SetDataFixedFloat(bGPU, 1.0F); bGPU->SetDataFixed(1);
/* call sum function */ /* call sum function */
_SumDim(aGPU, bGPU, cGPU, 1); _SumDim(aGPU, bGPU, cGPU, 1);
......
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